Description

Estimates Allele Frequency on qPCR DeltaDeltaCq from Bulk Samples.

Description

Interval estimation of the population allele frequency from qPCR analysis based on the restriction enzyme digestion (RED)-DeltaDeltaCq method (Osakabe et al. 2017, <doi:10.1016/j.pestbp.2017.04.003>), as well as general DeltaDeltaCq analysis. Compatible with the Cq measurement of DNA extracted from multiple individuals at once, so called "group-testing", this model assumes that the quantity of DNA extracted from an individual organism follows a gamma distribution. Therefore, the point estimate is robust regarding the uncertainty of the DNA yield.

README.md

cran.r-project.org

freqpcr

Interval estimation of population allele frequency based on quantitative PCR ΔΔCq measures from bulk samples

README in Japanese（日本語: equivalent to this document + vignette）

Features

Interval estimation of the population allele frequency from qPCR analysis based on the restriction enzyme digestion (RED)-ΔΔCq method (Osakabe et al. 2017), as well as general ΔΔCq analysis. Compatible with the Cq measurement of DNA extracted from multiple individuals at once, so called ``group-testing'', this model assumes that the quantity of DNA extracted from an individual organism follows a gamma distribution. Therefore, the point estimate is robust regarding the uncertainty of the DNA yield.

See the package vignette [Introduction to freqpcr] and published article (Sudo and Osakabe 2021 Molecular Ecology Resources) for the model structure.

Prerequisites are:

Want to know the percentage of the specific allele at the population level (population allele frequency) relative to other allele(s) on the same locus.
For the population in question, multiple DNA solutions (bulk samples) have been obtained, each of which consists of multiple individuals extracted at once.
Quantitative PCR based on the real-time PCR analysis has been performed on each bulk sample and a set of four Cq values is available.
The ΔΔCq value calculated from the Cq quartet represents the allele frequency of the bulk sample (sample allele frequency).

The main function freqpcr() takes as input data the number of individuals included in each bulk sample (N) and the set of Cq values (housek0, target0, housek1, target1) measured for each bulk sample (since it is a ΔΔCq method, there are usually four Cq values).

Prior to the estimation with samples with unknown allele ratios, auxiliary experimental parameters e.g. amplification efficiency during real-time PCR are required. Functions for estimating experimental parameters (knownqpcr() and knownqpcr_unpaired() functions) are also provided, using multiple DNA solutions with known allele mixing ratios.

Since v0.4.0, the freqpcr() function has been extended to accept datasets lacking control samples (housek0 and target0) i.e., a ΔCq method provided the sizes of the auxiliary experimental parameters are known.

Requirement

R (>= 3.6)
cubature (https://cran.r-project.org/package=cubature)

Installation

library(remotes)
install_github("sudoms/freqpcr")

library(freqpcr)
packageVersion("freqpcr")

If there are errors (converted from warning), it might be the case the dependent package 'cubature' has been built on a newer version of R (https://github.com/r-lib/remotes/issues/403).

** byte-compile and prepare package for lazy loading
Error: (converted from warning) package 'cubature' was built under R version 3.6.3

Then, set the following environment variable: R_REMOTES_NO_ERRORS_FROM_WARNINGS="true" on you R session and run install_github() again.

Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS="true")
install_github("sudoms/freqpcr")

Usage

library(freqpcr)

First, define the parameters for dummy data generation

Dummy Cq data is generated for demo use. As for real qPCR experiments, you first need to estimate those auxiliary parameters using samples with known multiple allele-mixing ratios and knownqpcr() or knownqpcr_unpaired() functions. See the vignette for detail.

P <- 0.15
K <- 4

# These auxiliary parameters must be pre-defined 
EPCR <- 0.97
zeroAmount <- 0.0016
scaleDNA <- 1e-06 # used in make_dummy() but not in freqpcr()
targetScale <- 1.2 
baseChange <- 0.2# used in make_dummy() but not in freqpcr()
sdMeasure <- 0.2

The population allele frequency to be estimated

The gamma shape parameter of the individual DNA yield

EPCR:

Amplification efficiency per PCR cycle, given as a positive numeric.
When EPCR = 1, template DNA doubles every cycle (EPCR + 1 = 2).

zeroAmount:

(In RED-ΔΔCq method) residual rate of restriction enzyme digestion.
(in general ΔΔCq and ΔCq analyses) small portion of the off-target allele amplified in the PCR process.
It needs to be always specified by the user as a number between 0 and 1, usually near 0.

scaleDNA:

Relative amount of the template DNA (of the housekeeping gene) compared to the threshold of realtime PCR.
If DNA increases twofold in a PCR cycle (EPCR = 1), scaleDNA = 10^-6 means Cq value is approximately 20.

targetScale:

Relative DNA amount of the target locus compared with the housekeeping gene.

baseChange:

Relative amount of the template DNA (of the housekeeping gene) before and after the restriction enzyme treatment in the RED-ΔΔCq method (as an involuntary change due to experimental manipulation).
The variable does not exist in other common ΔΔCq and ΔCq methods.

sdMeasure:

The measurement error on each Cq (Ct) value, following Normal(0, sdMeasure)

Make a dummy Cq dataset

With four bulk samples, each of which comprises eight haploid individuals

dmy_cq <- make_dummy(   rand.seed=71, P=P, K=K, ntrap=4, npertrap=8,
                        scaleDNA=scaleDNA, targetScale=targetScale, 
                        baseChange=targetScale, EPCR=EPCR, 
                        zeroAmount=zeroAmount,
                        sdMeasure=sdMeasure, diploid=FALSE  )
print(dmy_cq)

> print(dmy_cq)
An object of class "CqList"
Slot "N":
[1] 8 8 8 8

Slot "m":
     [,1] [,2] [,3] [,4]
[1,]    1    1    1    0
[2,]    7    7    7    8

Slot "xR":
[1] 2.661931e-06 6.163087e-06 3.199384e-06 0.000000e+00

Slot "xS":
[1] 2.216434e-05 3.163697e-05 3.221190e-05 4.018694e-05

Slot "housek0":
[1] 15.52662 14.86406 15.09659 14.84089

Slot "target0":
[1] 15.61207 14.80267 14.53848 14.85648

Slot "housek1":
[1] 17.85924 17.04084 17.54948 17.38438

Slot "target1":
[1] 21.08799 19.55396 20.92745 26.29820

Slot "DCW":
[1]  0.08544953 -0.06139074 -0.55811003  0.01559466

Slot "DCD":
[1] 3.228745 2.513126 3.377968 8.913814

Slot "deldel":
[1] 3.143296 2.574517 3.936078 8.898219

Slot "RFreqMeasure":
[1] 0.11868765 0.17453873 0.06933585 0.00239759

Slot "ObsP":
[1] 0.11868765 0.17453873 0.06933585 0.00239759

Slot "rand.seed":
[1] 71

Estimation with freqpcr

When P, K, sdMeasure, and targetScale are marked unknown.

result <- freqpcr(  N=dmy_cq@N, housek0=dmy_cq@housek0, target0=dmy_cq@target0,
                    housek1=dmy_cq@housek1, target1=dmy_cq@target1,
                    EPCR=EPCR, zeroAmount=zeroAmount, beta=TRUE, print.level=2  )
print(result)

> print(result)
An object of class "CqFreq"
Slot "report":
                                                 Estimate Fixed  (scaled) (scaled.SE)       2.5%       97.5%
P (R-allele frequency)                         0.09773189     0 -2.222684  0.60914923 0.03178086   0.2633220
K (gamma shape parameter)                     20.92728471     0  3.041054  1.83522515 0.57354355 763.5884712
targetScale (relative amount of target locus)  1.11922896     0  0.112640  0.08911953 0.93985370   1.3328388
Cq measurement error (SD)                      0.20973065     0 -1.561931  0.32845070 0.11017528   0.3992451
EPCR (Duplication efficiency of PCR)           0.97000000     1        NA          NA         NA          NA

Slot "obj":
$minimum
[1] 6.094915

$estimate
[1] -2.222684  3.041054  0.112640 -1.561931

$gradient
[1] -3.400973e-05 -8.275889e-05 -5.170087e-05  8.878422e-05

$hessian
            [,1]        [,2]       [,3]       [,4]
[1,]  2.71023719  0.05094362   1.168535 -0.1766756
[2,]  0.05094362  0.37630056   2.045198 -0.6539723
[3,]  1.16853469  2.04519835 147.389558  6.4578190
[4,] -0.17667556 -0.65397228   6.457819 11.1504636

$code
[1] 1

$iterations
[1] 12


Slot "cal.time":
   user  system elapsed
   0.72    0.15    0.88

When you have knowledge on the exact size of K (= set as a fixed parameter).

result <- freqpcr(  N=dmy_cq@N, housek0=dmy_cq@housek0, target0=dmy_cq@target0,
                    housek1=dmy_cq@housek1, target1=dmy_cq@target1,
                    K=4,
                    EPCR=EPCR, zeroAmount=zeroAmount, beta=TRUE, print.level=1  )

External links

GitHub repository https://github.com/sudoms/freqpcr
PDF help of the latest version https://github.com/sudoms/freqpcr/releases/latest
Paper https://doi.org/10.1111/1755-0998.13554
Sudo, M., & Osakabe, M. (2021). freqpcr: Estimation of population allele frequency using qPCR ΔΔCq measures from bulk samples. Molecular Ecology Resources, 00, 1–14. Creative Commons Attribution 4.0 International (CC BY 4.0)
Author website https://sudori.info/english.html

citation

citation("freqpcr")

Author

Masaaki Sudo (https://orcid.org/0000-0001-9834-9857) National Agriculture and Food Research Organization (NARO), Japan

License

GNU GPL (>= 3)

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.

History

NEWS.md.

r-freqpcr

freqpcr

Features

Requirement

Installation

Usage

First, define the parameters for dummy data generation

Make a dummy Cq dataset

Estimation with freqpcr

External links

citation

Author

License

History

Version

License

Status

Source

Homepage

Platforms (75)

freqpcr

Features

Requirement

Installation

Usage

First, define the parameters for dummy data generation

Make a dummy Cq dataset

Estimation with freqpcr

External links

citation

Author

License

History

Version

License

Status

Source

Homepage

Platforms75 (75)

Platforms (75)